No Evidence for Recent Selection at FOXP2 among Diverse Human Populations.

FOXP2, initially identified for its role in human speech, contains two nonsynonymous substitutions derived in the human lineage. Evidence for a recent selective sweep in Homo sapiens, however, is at odds with the presence of these substitutions in archaic hominins. Here, we comprehensively reanalyze FOXP2 in hundreds of globally distributed genomes to test for recent selection. We do not find evidence of recent positive or balancing selection at FOXP2. Instead, the original signal appears to have been due to sample composition. Our tests do identify an intronic region that is enriched for highly conserved sites that are polymorphic among humans, compatible with a loss of function in humans. This region is lowly expressed in relevant tissue types that were tested via RNA-seq in human prefrontal cortex and RT-PCR in immortalized human brain cells. Our results represent a substantial revision to the adaptive history of FOXP2, a gene regarded as vital to human evolution.

adaPop: Bayesian inference of dependent population dynamics in coalescent models.

The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.

Distance metrics for ranked evolutionary trees.

Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels unlock opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked genealogies facilitate the study of evolutionary processes of different organisms sampled at multiple time periods. We propose metrics on ranked tree shapes and ranked genealogies for lineages isochronously and heterochronously sampled. Our proposed tree metrics make it possible to conduct statistical analyses of ranked tree shapes and timed ranked tree shapes or ranked genealogies. Such analyses allow us to assess differences in tree distributions, quantify estimation uncertainty, and summarize tree distributions. We show the utility of our metrics via simulations and an application in infectious diseases.

Statistical Challenges in Tracking the Evolution of SARS-CoV-2.

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.

Enumeration of binary trees compatible with a perfect phylogeny.

Evolutionary models used for describing molecular sequence variation suppose that at a non-recombining genomic segment, sequences share ancestry that can be represented as a genealogy-a rooted, binary, timed tree, with tips corresponding to individual sequences. Under the infinitely-many-sites mutation model, mutations are randomly superimposed along the branches of the genealogy, so that every mutation occurs at a chromosomal site that has not previously mutated; if a mutation occurs at an interior branch, then all individuals descending from that branch carry the mutation. The implication is that observed patterns of molecular variation from this model impose combinatorial constraints on the hidden state space of genealogies. In particular, observed molecular variation can be represented in the form of a perfect phylogeny, a tree structure that fully encodes the mutational differences among sequences. For a sample of n sequences, a perfect phylogeny might not possess n distinct leaves, and hence might be compatible with many possible binary tree structures that could describe the evolutionary relationships among the n sequences. Here, we investigate enumerative properties of the set of binary ranked and unranked tree shapes that are compatible with a perfect phylogeny, and hence, the binary ranked and unranked tree shapes conditioned on an observed pattern of mutations under the infinitely-many-sites mutation model. We provide a recursive enumeration of these shapes. We consider both perfect phylogenies that can be represented as binary and those that are multifurcating. The results have implications for computational aspects of the statistical inference of evolutionary parameters that underlie sets of molecular sequences.

Exact limits of inference in coalescent models.

Recovery of population size history from molecular sequence data is an important problem in population genetics. Inference commonly relies on a coalescent model linking the population size history to genealogies. The high computational cost of estimating parameters from these models usually compels researchers to select a subset of the available data or to rely on insufficient summary statistics for statistical inference. We consider the problem of recovering the true population size history from two possible alternatives on the basis of coalescent time data previously considered by Kim et al. (2015). We improve upon previous results by giving exact expressions for the probability of correctly distinguishing between the two hypotheses as a function of the separation between the alternative size histories, the number of individuals, loci, and the sampling times. In more complicated settings we estimate the exact probability of correct recovery by Monte Carlo simulation. Our results give considerably more pessimistic inferential limits than those previously reported. We also extended our analyses to pairwise SMC and SMC' models of recombination. This work is relevant for optimal design when the inference goal is to test scientific hypotheses about population size trajectories in coalescent models with and without recombination.

Uric Acid Crystals Induce Placental Inflammation and Alter Trophoblast Function via an IL-1-Dependent Pathway: Implications for Fetal Growth Restriction.

Excessive placental inflammation is associated with several pathological conditions, including stillbirth and fetal growth restriction. Although infection is a known cause of inflammation, a significant proportion of pregnancies have evidence of inflammation without any detectable infection. Inflammation can also be triggered by endogenous mediators, called damage associated molecular patterns or alarmins. One of these damage-associated molecular patterns, uric acid, is increased in the maternal circulation in pathological pregnancies and is a known agonist of the Nlrp3 inflammasome and inducer of inflammation. However, its effects within the placenta and on pregnancy outcomes remain largely unknown. We found that uric acid (monosodium urate [MSU]) crystals induce a proinflammatory profile in isolated human term cytotrophoblast cells, with a predominant secretion of IL-1ß and IL-6, a result confirmed in human term placental explants. The proinflammatory effects of MSU crystals were shown to be IL-1-dependent using a caspase-1 inhibitor (inhibits IL-1 maturation) and IL-1Ra (inhibits IL-1 signaling). The proinflammatory effect of MSU crystals was accompanied by trophoblast apoptosis and decreased syncytialization. Correspondingly, administration of MSU crystals to rats during late gestation induced placental inflammation and was associated with fetal growth restriction. These results make a strong case for an active proinflammatory role of MSU crystals at the maternal-fetal interface in pathological pregnancies, and highlight a key mediating role of IL-1. Furthermore, our study describes a novel in vivo animal model of noninfectious inflammation during pregnancy, which is triggered by MSU crystals and leads to reduced fetal growth.

Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference.

Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples.

Novel Noncompetitive IL-1 Receptor-Biased Ligand Prevents Infection- and Inflammation-Induced Preterm Birth.

Preterm birth (PTB) is firmly linked to inflammation regardless of the presence of infection. Proinflammatory cytokines, including IL-1ß, are produced in gestational tissues and can locally upregulate uterine activation proteins. Premature activation of the uterus by inflammation may lead to PTB, and IL-1 has been identified as a key inducer of this condition. However, all currently available IL-1 inhibitors are large molecules that exhibit competitive antagonism properties by inhibiting all IL-1R signaling, including transcription factor NF-κB, which conveys important physiological roles. We hereby demonstrate the efficacy of a small noncompetitive (all-d peptide) IL-1R-biased ligand, termed rytvela (labeled 101.10) in delaying IL-1ß-, TLR2-, and TLR4-induced PTB in mice. The 101.10 acts without significant inhibition of NF-κB, and instead selectively inhibits IL-1R downstream stress-associated protein kinases/transcription factor c-jun and Rho GTPase/Rho-associated coiled-coil-containing protein kinase signaling pathways. The 101.10 is effective at decreasing proinflammatory and/or prolabor genes in myometrium tissue and circulating leukocytes in all PTB models independently of NF-κB, undermining NF-κB role in preterm labor. In this work, biased signaling modulation of IL-1R by 101.10 uncovers a novel strategy to prevent PTB without inhibiting NF-κB.

An efficient Bayesian inference framework for coalescent-based nonparametric phylodynamics.

MOTIVATION: The field of phylodynamics focuses on the problem of reconstructing population size dynamics over time using current genetic samples taken from the population of interest. This technique has been extensively used in many areas of biology but is particularly useful for studying the spread of quickly evolving infectious diseases agents, e.g. influenza virus. Phylodynamic inference uses a coalescent model that defines a probability density for the genealogy of randomly sampled individuals from the population. When we assume that such a genealogy is known, the coalescent model, equipped with a Gaussian process prior on population size trajectory, allows for nonparametric Bayesian estimation of population size dynamics. Although this approach is quite powerful, large datasets collected during infectious disease surveillance challenge the state-of-the-art of Bayesian phylodynamics and demand inferential methods with relatively low computational cost. RESULTS: To satisfy this demand, we provide a computationally efficient Bayesian inference framework based on Hamiltonian Monte Carlo for coalescent process models. Moreover, we show that by splitting the Hamiltonian function, we can further improve the efficiency of this approach. Using several simulated and real datasets, we show that our method provides accurate estimates of population size dynamics and is substantially faster than alternative methods based on elliptical slice sampler and Metropolis-adjusted Langevin algorithm. AVAILABILITY AND IMPLEMENTATION: The R code for all simulation studies and real data analysis conducted in this article are publicly available at http://www.ics.uci.edu/∼slan/lanzi/CODES.html and in the R package phylodyn available at https://github.com/mdkarcher/phylodyn. CONTACT: S.Lan@warwick.ac.uk or babaks@uci.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Sterile inflammation and pregnancy complications: a review.

Inflammation is essential for successful embryo implantation, pregnancy maintenance and delivery. In the last decade, important advances have been made in regard to endogenous, and therefore non-infectious, initiators of inflammation, which can act through the same receptors as pathogens. These molecules are referred to as damage-associated molecular patterns (DAMPs), and their involvement in reproduction has only recently been unraveled. Even though inflammation is necessary for successful reproduction, untimely activation of inflammatory processes can have devastating effect on pregnancy outcomes. Many DAMPs, such as uric acid, high-mobility group box 1 (HMGB1), interleukin (IL)-1 and cell-free fetal DNA, have been associated with pregnancy complications, such as miscarriages, preeclampsia and preterm birth in preclinical models and in humans. However, the specific contribution of alarmins to these conditions is still under debate, as currently there is lack of information on their mechanism of action. In this review, we discuss the role of sterile inflammation in reproduction, including early implantation and pregnancy complications. Particularly, we focus on major alarmins vastly implicated in numerous sterile inflammatory processes, such as uric acid, HMGB1, IL-1α and cell-free DNA (especially that of fetal origin) while giving an overview of the potential role of other candidate alarmins.

Statistical summaries of unlabelled evolutionary trees.

Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, relatively less literature exists for unlabelled trees that are increasingly useful, for example when one seeks to summarize samples of trees obtained with different methods, or from different samples and environments, and wishes to assess the stability and generalizability of these summaries. In our paper, we exploit recently proposed distance metrics of unlabelled ranked binary trees and unlabelled ranked genealogies, or trees equipped with branch lengths, to define the Fréchet mean, variance and interquartile sets as summaries of these tree distributions. We provide an efficient combinatorial optimization algorithm for computing the Fréchet mean of a sample or of distributions on unlabelled ranked tree shapes and unlabelled ranked genealogies. We show the applicability of our summary statistics for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020. Our current implementations are publicly available at https://github.com/RSamyak/fmatrix.

Patient-Reported Outcome Measures in Routine Clinical Practice: Practical Guidance for Institutional Review Boards.

The use of patient-reported outcome measures (PROMs) is increasingly common in routine clinical practice. As tools to quantify symptoms and health status, PROMs play an important role in focusing health care on outcomes that matter to patients. The uses of PROM data are myriad, ranging from clinical care to survey-based research and quality improvement. Discerning the boundaries between these use cases can be challenging for institutional review boards (IRBs). In this article, we provide a framework for classifying the three primary PROM use cases (clinical care, human subjects research, and quality improvement) and discuss the level of IRB oversight (if any) necessary for each. One of the most important considerations for IRB staff is whether PROMs are being used primarily for clinical care and thus do not constitute human subjects research. We discuss characteristics of PROMs implemented primarily for clinical care, focusing on: data platform; survey location; questionnaire length; patient interface; and clinician interface. We also discuss IRB oversight of projects involving the secondary use of PROM data that were collected during the course of clinical care, which span human subjects research and quality improvement. This framework provides practical guidance for IRB staff as well as clinicians who use PROMs as communication aids in routine clinical practice.

The subcutaneous adipose transcriptome identifies a molecular signature of insulin resistance shared with visceral adipose.

OBJECTIVE: The objective of this study was to identify the transcriptional landscape of insulin resistance (IR) in subcutaneous adipose tissue (SAT) in humans across the spectrum of obesity. METHODS: We used SAT RNA sequencing in 220 individuals with metabolic phenotyping. RESULTS: We identified a 35-gene signature with high predictive accuracy for homeostatic model of IR that was expressed across a variety of non-immune cell populations. We observed primarily "protective" IR associations for adipocyte transcripts and "deleterious" associations for macrophage transcripts, as well as a high concordance between SAT and visceral adipose tissue (VAT). Multiple SAT genes exhibited dynamic expression 5 years after weight loss surgery and with insulin stimulation. Using available expression quantitative trait loci in SAT and/or VAT, we demonstrated similar genetic effect sizes of SAT and VAT on type 2 diabetes and BMI. CONCLUSIONS: SAT is conventionally viewed as a metabolic buffer for lipid deposition during positive energy balance, whereas VAT is viewed as a dominant contributor to and prime mediator of IR and cardiometabolic disease risk. Our results implicate a dynamic transcriptional architecture of IR that resides in both immune and non-immune populations in SAT and is shared with VAT, nuancing the current VAT-centric concept of IR in humans.

Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies.

Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method.

Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size N e (t), a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on N e (t), the coalescent and the sampling processes can be jointly modeled to improve estimation of N e (t). Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of N e (t) and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.

The impact of the COVID-19 preventive measures on influenza transmission: molecular and epidemiological evidence.

OBJECTIVE: We quantify the impact of COVID-19-related control measures on the spread of human influenza virus H1N1 and H3N2. METHODS: We analyzed case numbers to estimate the end of the 2019-2020 influenza season and compared it with the median of the previous 9 seasons. In addition, we used influenza molecular data to compare within-region and between-region genetic diversity and effective population size from 2019 to 2020. Finally, we analyzed personal behavior and policy stringency data for each region. RESULTS: The 2019-2020 influenza season ended earlier than the median of the previous 9 seasons in all regions. For H1N1 and H3N2, there was an increase in between-region genetic diversity in most pairs of regions between 2019 and 2020. There was a decrease in within-region genetic diversity for 12 of 14 regions for H1N1 and 9 of 12 regions for H3N2. There was a decrease in effective population size for 10 of 13 regions for H1N1 and 3 of 7 regions for H3N2. CONCLUSIONS: We found consistent evidence of a decrease in influenza incidence after the introduction of preventive measures due to COVID-19 emergence.

Adaptive preferential sampling in phylodynamics.

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size $N_{e}(t)$, a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on $N_{e}(t)$, the coalescent and the sampling processes can be jointly modeled to improve estimation of $N_{e}(t)$. Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of $N_{e}(t)$ and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide scalable algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref.